dplyr VS Pandas

Compare dplyr vs Pandas and see what are their differences.


Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more (by pandas-dev)
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dplyr Pandas
40 399
4,670 42,104
0.8% 0.9%
7.1 10.0
4 days ago 6 days ago
R Python
GNU General Public License v3.0 or later BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of dplyr. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-15.


Posts with mentions or reviews of Pandas. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-08.

What are some alternatives?

When comparing dplyr and Pandas you can also consider the following projects:

worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob

Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis

Rustler - Safe Rust bridge for creating Erlang NIF functions

tensorflow - An Open Source Machine Learning Framework for Everyone

ggplot2 - An implementation of the Grammar of Graphics in R

orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis

nx - Multi-dimensional arrays (tensors) and numerical definitions for Elixir

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir

Keras - Deep Learning for humans

rmarkdown - Dynamic Documents for R

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration